I was amazed at the VitalSource way of presenting the books....
Everything looks perfectly typeset, but yet you can "flip"
through the book in the same way you would "flip" through a very
long web page in your web browser. And best of all, whenever I
have my tablet with me, my books are just a swipe away.

Comment from the Stata technical group

Meta-analysis has gained increasing popularity since the early 1990s
as a way to synthesize the results from separate studies. It is widely used
in the medical sciences, education, and business. This text is both
complete and current, and is ideal for researchers wanting a conceptual
treatment of the methodology. A chapter on statistical software for
performing meta-analysis (including how to do so in Stata) is also included.

Introduction
Definition of a summary effect
Estimating the summary effect
Extreme effect size in a large study or a small study
Confidence interval
The null hypothesis
Which model should we use?
Model should not be based on the test for heterogeneity
Concluding remarks
Summary points

Introduction
A conceptual approach
In context
When to use power analysis
Planning for precision rather than for power
Power analysis in primary studies
Power analysis for meta-analysis
Power analysis for a test of homogeneity
Summary points

30 Publication bias

Introduction
The problem of missing studies
Methods for addressing bias
Illustrative example
The model
Getting a sense of the data
Is there evidence of any bias?
Is the entire effect an artifact of bias?
How much of an impact might the bias have?
Summary of the findings for the illustrative example
Some important caveats
Small-study effects
Concluding remarks
Summary points

Part 7: Issues related to effect size

31 Overview

32 Effect sizes rather than p-values

Introduction
Relationship between p-values and effect sizes
The distinction is important
The p-value is often misinterpreted
Narrative reviews vs. meta-analyses
Summary points

33 Simpson’s paradox

Introduction
Circumcision and risk of HIV infection
An example of the paradox
Summary points

Introduction
One number cannot summarize a research field
The file drawer problem invalidates meta-analysis
Mixing apples and oranges
Garbage in, garbage out
Important studies are ignored
Meta-analysis can disagree with randomized trials
Meta-analyses are performed poorly
Is a narrative review better?
Concluding remarks
Summary points